OverviewWho we're looking for Toyota's Data Science department is looking for an experienced technical leader to manage the team that builds and operates production-grade machine learning, analytics, optimization, and decision-support systems. This role leads the engineers behind ML-powered products across credit, pricing, collections, treasury, and other business functions, setting technical direction, owning delivery, and ensuring these capabilities operate as end-to-end decision systems that balance technical performance, business value, operational reliability, and governance.
Reporting to the National Manager, Data Science, you will partner with data science and business leaders and cross-functional technology teams to translate business priorities into intelligent, data-driven capabilities. You will set the team's technical bar and delivery rhythm, helping engineers move quickly without compromising quality or operational readiness. You will remain selectively hands-on where your judgment matters most, shaping architecture, challenging assumptions, and guiding high-impact designs while empowering the team to own execution and innovate.
Most importantly, you are a people leader who coaches engineers and senior ICs, gives direct and actionable feedback, grows technical ownership, and builds a team environment where engineers produce thoughtful, durable work.
What you'll be doing - Hire, coach, and mentor Machine Learning Engineers and senior engineers. Create intentional development opportunities for both ICs and those who may grow into leadership. Build a culture of ownership, continuous improvement, and constructive feedback.
- Guide architecture, testing, deployment, observability, drift detection and revalidation, data quality, and production-readiness standards. Treat ML systems differently from ordinary software by designing for model and data drift, champion/challenger evaluation, clear revalidation triggers, strong lineage, and auditability. Steer designs through sharp questions about failure modes, performance, and governance.
- Collaborate with data scientists, analysts, data engineers, product managers, risk and finance partners, and technology teams to translate business needs, which are often ambiguous or regulated, into clear technical plans. Work with data science leadership to establish clear handoff and validation criteria for prototypes, ensuring that experimental models can be hardened, governed, and deployed efficiently. Drive consensus by framing options, risks, and recommendations in plain language.
- Oversee the design and implementation of high-throughput services, batch pipelines, optimization and operations research engines, such as MILP, and analytics applications on AWS, Snowflake, or comparable platforms. Evaluate emerging techniques such as generative AI, simulation, or advanced forecasting when they provide measurable business value, and integrate them responsibly with proper governance. Ensure systems meet reliability, reproducibility, auditability, and performance targets.
- Sequence model development, platform improvements, and reliability work; clarify ownership boundaries between data science, ML engineering, and other technology teams; and balance short-term experimentation with long-term platform leverage.
- Run design reviews, code reviews, release checklists, and team processes that prioritize maintainability, reproducibility, safety, and audit-ready documentation. Champion responsible AI practices, including model explainability, bias and fairness considerations, and reproducible decision logic.
- Introduce stronger MLOps practices, including reusable patterns, CI/CD improvements, automated testing, monitoring and alerting, reproducibility checks, and robust incident response. Help build internal frameworks, templates, and golden paths that make high-quality delivery repeatable.
- Balance new development with maintenance and technical debt. Drive prioritization across domains and stakeholders by weighing business value, urgency, risk, and technical effort. Manage tradeoffs among speed, quality, and long-term operating cost, and ensure the team is building the right capabilities in the right order.
- Communicate status, risks, and design decisions to peers and leadership. Contribute to planning and budgeting discussions. Influence strategy outside your reporting line when needed.
What you bring - Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Operations Research, or a related technical field, or equivalent practical experience.
- 7+ years of professional experience in data science, machine learning engineering, or applied ML, with hands-on ownership of production systems and data-intensive applications.
- 2+ years of people-management experience, or equivalent experience leading technical teams, with responsibility for coaching, performance feedback, and delivery ownership, along with a track record of developing engineers and mentoring senior ICs to create environments where teams make thoughtful tradeoffs and deliver durable systems.
- Demonstrated experience building, deploying, and operating machine learning or optimization systems in production, with ownership across the full lifecycle from design through monitoring, drift management, and retraining in the cloud.
- Proficiency with Python and SQL, along with hands-on experience using cloud platforms such as AWS, GCP, or Azure and modern data technologies such as Snowflake, Spark, or Databricks.
- Experience establishing or improving engineering processes such as code review, design review, spec driven development, testing strategy, production readiness, monitoring, documentation, and post-incident review to raise team standards.
- Experience and knowledge to ask how a decision can be demonstrated to be correct, reproducible, and defensible before shipping, along with comfort operating in environments where models carry audit and regulatory exposure.
- Experience managing delivery across multiple projects, stakeholders, and business domains, while balancing urgency, risk, compliance, and technical debt.
- Experience with writing clear design documents, present technical options and tradeoffs, and provide executive-level updates.
Added bonus if you have - Master's or higher in a quantitative or technical discipline (CS, Engineering, Data Science, Statistics, Mathematics, Operations Research, etc.).
- Domain experience in regulated decisioning (lending, insurance, fraud, risk, pricing) and the governance and auditability practices that come with it.
- Advanced MLOps experience: CI/CD, model registries, containerization (Docker, Kubernetes), infrastructure-as-code, automated drift detection, data validation, or deployment governance.
- Generative AI application experience: LLM-powered workflows, RAG, semantic search, evaluation, guardrails, monitoring, or responsible-AI practices.
- Experience building reusable internal platforms, frameworks, templates, or golden paths that improved engineering quality across teams.
- AWS Certified Machine Learning Engineer - Associate, Solutions Architect, Developer, or equivalent.
What we'll bring During your interview process, our team can fill you in on all the details of our industry-leading benefits and career development opportunities. A few highlights include:
- A work environment built on teamwork, flexibility and respect
- Professional growth and development programs to help advance your career, as well as tuition
- reimbursement
- Team Member Vehicle Purchase Discount
- Toyota Team Member Lease Vehicle Program (if applicable)
- Comprehensive health care and wellness plans for your entire family
- Toyota 401(k) Savings Plan featuring a company match, as well as an annual retirement contribution from
- Toyota regardless of whether you contribute
- Paid holidays and paid time off
- Referral services related to prenatal services, adoption, childcare, schools and more
- Tax Advantaged Accounts (Health Savings Account, Health Care FSA, Dependent Care FSA)
- Relocation assistance (if applicable)
